class_docstr: Apply multiplicative 1-centered Gaussian noise.
  
  As it is a regularization layer, it is only active at training time.
  
  Args:
      rate: Float, drop probability (as with `Dropout`).
          The multiplicative noise will have
          standard deviation `sqrt(rate / (1 - rate))`.
      seed: Integer, optional random seed to enable deterministic behavior.
  
  Call arguments:
      inputs: Input tensor (of any rank).
      training: Python boolean indicating whether the layer should behave in
          training mode (adding dropout) or in inference mode (doing nothing).
code: def call(self, inputs, training=False):
      if training and self.rate > 0:
          stddev = math.sqrt(self.rate / (1.0 - self.rate))
          return inputs * backend.random.normal(
              shape=ops.shape(inputs),
              mean=1.0,
              stddev=stddev,
              seed=self.seed_generator,
          )
      return inputs
  
file_path: keras/keras/layers/regularization/gaussian_dropout.py
imports: import math
  from keras import backend
  from keras import layers
  from keras import ops
  from keras.api_export import keras_export
label_desc: Use image node_id 2 for calling the GaussianDropout obj's underlying member method code with example usage: obj.call(inputs, training) and returns: inputs, unknown